A normal index maps documents to words. An inverted index maps words to documents. That reversal is why search is fast.
Two users update the same row. Pessimistic locking blocks one until the other finishes. Optimistic locking lets both try and fails the loser. Choosing wrong kills either throughput or correctness.
Two-phase commit guarantees atomicity across multiple databases. It also blocks everything if the coordinator dies. Here’s why microservices moved on.
Auto-increment IDs break the moment you have more than one database. Snowflake IDs, UUIDs, and database sequences each solve this differently.
A follows table with two columns seems trivial. Until you need to query it from both directions, across shards, for millions of users.
OFFSET 50000 makes MySQL scan 50,000 rows just to skip them. Cursor pagination stays fast no matter how deep you go.
You added read replicas to scale reads. Now users update their profile and see the old version. Welcome to replica lag.
ALTER TABLE on a 2M row table locks it for minutes. Your users see errors. Here’s how expand-contract and shadow writes let you migrate without downtime.
The hidden cost of database connections. How connection pools work, why they matter, and how to size them without guessing.
Why the CAP Theorem is the most misunderstood rule in system design. Addressing the ‘Pick 2’ lie and how it sets the stage for consensus algorithms.
Why standard views are just aliases and how materialized views act as an ‘in-database cache’ to solve the cross-shard query problem.
How to capture and stream database changes in real-time. CDC patterns, implementation approaches, and when to use it instead of application-level events.
How to partition database across multiple servers. Hash-based vs range-based sharding, rebalancing strategies, and the complexity that comes with it.
Bloom filters skip unnecessary disk reads in LSM trees by saying ‘definitely not here’ with zero false negatives. Learn how Cassandra and RocksDB use them.
LSM trees create SSTables fast but need compaction. Learn size-tiered vs leveled compaction strategies and the write vs read amplification tradeoff.
LSM Trees vs B-Trees: the write-fast or read-fast tradeoff. Learn when to use B-trees (MySQL) vs LSM trees (Cassandra) based on your database workload.
How do databases survive crashes and ensure durability? Learn how Write-Ahead Logging (WAL) uses sequential writes to guarantee data persistence without killing performance.
Stop staring at EXPLAIN output confused. Learn to read MySQL execution plans like a map and find the root cause of slow queries in seconds, not hours.
Querying partitioned databases by non-partition keys? Learn the tradeoffs between local and global secondary indexes in distributed systems.
Every JOIN multiplies query complexity. Learn the three JOIN strategies databases use and when denormalization beats JOIN performance by 30x.
More indexes don’t mean faster queries. Learn when to add, remove, and optimize database indexes. Real examples of 7x performance gains through strategic indexing.
Stop guessing at performance problems. Learn the 5-step systematic framework for debugging slow queries that helped reduce query times from 2+ seconds to 30ms.